Self-Doping Memristors with Equivalently Synaptic Ion Dynamics for

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Functional Inorganic Materials and Devices

Self-doping memristors with equivalently synaptic ion dynamics for neuromorphic computing Yaoyuan Wang, Ziyang Zhang, Mingkun Xu, Yifei Yang, Mingyuan Ma, Huanglong Li, Jing Pei, and Luping Shi ACS Appl. Mater. Interfaces, Just Accepted Manuscript • Publication Date (Web): 23 May 2019 Downloaded from http://pubs.acs.org on May 27, 2019

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ACS Applied Materials & Interfaces

Self-doping memristors with equivalently synaptic ion dynamics for neuromorphic computing Yaoyuan Wang, †, § Ziyang Zhang, †, § Mingkun Xu, †, § Yifei Yang, † Mingyuan Ma, ‡ Huanglong Li, † Jing Pei † and Luping Shi*, † †Department

of Precision Instrument, Center for Brain Inspired Computing Research, Beijing

Innovation Center for Future Chip, ‡Department of Electronic Engineering, Tsinghua University, Beijing, China, 100084 KEYWORDS: memristor, interface, synaptic plasticity, dynamics, neuromorphic computing

ABSTRACT: The accumulation and extrusion of Ca2+ ions in the pre- and post-synaptic terminals play crucial roles in initiating short- and long-term plasticity (STP and LTP) in biological synapses, respectively. Mimicking these synaptic behaviors by electronic devices represents a vital step towards realization of neuromorphic computing. However, the majority of reported synaptic devices usually focus on the emulation of qualitatively synaptic behaviors, devices that can truly emulate the physical behavior of the synaptic Ca2+ ions dynamics in STP and LTP are rarely reported. In this work, Ag/Ag:Ta2O5/Pt self-doping memristors were developed to equivalently emulate the Ca2+ ions dynamics of biological synapses. With conductive filaments from double sources, these memristors produced unique double switching behavior under voltage sweeps and demonstrated several essential synaptic behaviors under pulse stimuli, including STP, LTP, STP to LTP transition and spike-ratedependent plasticity (SRDP). Experimental results and nanoparticle dynamic simulations both 1 ACS Paragon Plus Environment

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showed that Ag atoms from double sources could mimic Ca2+ dynamics in the pre- and postsynaptic terminals under stimuli. A perceptron network with an STP to LTP transition layer based on the self-doping memristors was also introduced and evaluated; simulations showed that this network could solve noisy figure recognition tasks efficiently. All of these results indicate that the self-doping memristors are promising components for future hardware creation of neuromorphic systems and emulate the characteristics of the brain.

1. INTRODUCTION Brain memory and learning actions are believed to be performed via dynamic changes in the connection strengths (weights) of synapses between neurons.1 These dynamic weights changes are determined by spikes among the neural networks; this process is called synaptic plasticity.2-3 There are two types of synaptic plasticity, which act over different timescales: short-term plasticity (STP) and long-term plasticity (LTP).4-7 Repeated stimuli at short intervals can induce the transition from STP to LTP.8-9 Neuromorphic computing, which is inspired by the working principles of neural systems in the brain, is currently regarded as an emerging computing paradigm.10-13 It is a fundamental Non-von Neumann scheme that allows the computation to be performed at data locations, thereby overcoming the Von Neumann block.14 Achievement of neuromorphic computing systems requires the development of artificial synapses. In neuromorphic engineering, artificial synapses based on complementary metal-oxide-semiconductor (CMOS) circuits are complex and often require high power consumption.15-18 Many emerging devices have been reported to demonstrate synapse functions in previous studies, including memristor-based synapses, phase change synapses, ferroelectric synapses, 2D material synapses and other artificial synapses.19-30 Among these devices, two-terminal memristors offer the advantages of three-dimensional scalability, relatively low power consumption and compatibility with conventional CMOS circuit 2 ACS Paragon Plus Environment

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implementations;31-32 they are thus the most attractive candidate among these synapse devices. Besides, memristors can also act as the artificial neuron devices,33-35 which can assemble with memristor synapse devices to form high-density neural networks. However, emulation of the synaptic behaviors by memristors is currently concentrated at the qualitative demonstration level (electrical response) and does not allow the crucial subcellular traits of synapses, such as the Ca2+ ions dynamics, to be captured. While this qualitative emulation is simple and repeatable, its physical behavior differs significantly from that of the actual synapses, which thus limits the fidelity and variety of the desired synaptic functions. Recently, memristors with active mental diffusive dynamics were developed that could mimic synaptic Ca2+ ions dynamics,21,

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which could demonstrate a direct emulation of synaptic behaviors. Besides,

some attractive biological systems were also demonstrated by them, such as artificial nociceptors and neurons.

37-38

Neurobiology studies indicate that the formation of STP and

LTP in synapses is significantly related to the dynamics of the Ca2+ ions in pre- and postsynaptic terminals, respectively.39-41 The influx of the Ca2+ ions in pre-synaptic terminals can induce the synaptic connection enhancement, and the extrusion of them leads to the spontaneously decay of the connection. While an influx of the Ca2+ ions in post-synaptic terminals play an important role in the formation of long-term change of synapse connections. The volatile diffusive devices have pretty good threshold switching property of low energy consumption,21 however, these devices mainly focus on the emulation of Ca2+ ions in presynaptic terminals, which is related to the STP. Thus they are difficult to mimic the LTP without nonvolatile memristors. Other devices with volatile and nonvolatile behaviors19, 31, 42 using single Ca2+ ions emulator source are unable to distinguish between the Ca2+ ions in the pre- and post-synaptic terminals, which is different from the biological synapse. A device with similar physical behavior that is equivalent to the subcellular Ca2+ ions dynamics could enable improved emulation of synaptic functions, which implies the potential for synaptic 3 ACS Paragon Plus Environment


weight self-updating and autonomic computing like human brains, as well as the emulation of some novel synaptic functions which are determined by the ionic dynamics and discovered in the future, thus it would have broad applications in neuromorphic computing. In this work, we have developed Ag/Ag:Ta2O5/Pt self-doping memristors to mimic the functions of synapses with high levels of bio-fidelity. Different from the existing diffusive memristors,21, 36, 42-43 our self-doping memristors demonstrate a unique double sudden current jump behavior during the switching process. Unlike the mechanism of double switching memristors reported in previous works, such as double switching layer structure, phase transformation coupled with metallic filaments, ionic coupled with metallic filaments and multistage metal-insulator transition,44-47 the double switching behavior in our memristors is determined by their self-doping structures. Auger electron spectroscopy analysis showed that some Ag atoms can be doped into the insulating Ta2O5 layer during fabrication, and this can be regarded as a self-doping phenomenon. In the presence of an external voltage, these Ag atoms first form a weak conductive filament through a solid-state electrochemical reaction and diffusion process,43 which causes the first switching. This weak filament can be ruptured spontaneously in the absence of the external voltage, which leads to conductance decay and is similar to pre-synaptic Ca2+ ions dynamics in STP.39-41 On the other hand, under a higher voltage, Ag atoms from the Ag electrode can strengthen the existing filament or even form additional filaments, thus causing the second switching. These strong filaments can be maintained for long periods or even permanently and can thus mimic the post-synaptic Ca2+ ions dynamics in LTP.39-41 These dynamical properties of the Ag atoms from the self-doping source and the Ag electrode source can functionally equivalent to Ca2+ ions behaviors of preand post-terminals in biological synapses. Under appropriate pulse stimuli, our self-doping memristor can emulate the synapse functions of the transition between STP and LTP, the paired-pulse facilitation (PPF), post-tetanic potentiation (PTP) and spike-rate-dependent 4 ACS Paragon Plus Environment

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plasticity (SRDP). For neuromorphic computing applications, a neural network containing an STP-to-LTP transition layer based on our device was introduced and evaluated. Simulation results showed that this network could solve noisy classification tasks more effectively than single perceptron networks. 2. RESULTS AND DISCUSSION 2.1 Double Switching Behaviors of Self-doping Memristors. A schematic diagram showing the cross-section of the Ag/Ag:Ta2O5/Pt self-doping memristor is shown in Figure 1a. An optical microscope image of the device is shown in Figure 1b. The device junction area is 4 μm2. The current-voltage (I-V) characteristics of the device were measured by applying voltage sweeps to the Ag bottom electrode while the Pt top electrode was grounded. The pristine device was in the high-resistance state (HRS). As illustrated in Figure 1c, during a positive voltage sweep from 0 V to 0.6 V then back to 0 V, the device clearly showed two sudden current jumps during the switching process. We intentionally applied an external compliance current (CC) of 1 mA during the sweep to protect the device from hard breakdown. When the voltage reached ~0.2 V, the first switching event occurred, and the current suddenly jumped from ~0.1 nA to ~30 nA. The device changed from the HRS to the middle-resistance state (MRS). Then, the current increased smoothly until the second switching event occurred at ~0.39 V, where the current suddenly jumped from ~0.1 μA to ~100 μA. The device changed here from the MRS to the low-resistance state (LRS). After the second switching event, the current increased less sharply with increasing voltage. This double-switching behavior differs from that of typical reported Ag/metal-oxide based memristors,43 which have only one switching event in their SET process (the process in which the device changes from the HRS to the LRS). It should also be noted that our self-doping memristors did not require forming process. After the positive sweep, a negative sweep from 5 ACS Paragon Plus Environment


0 V to −0.6 V and then back to 0 V was applied. The current decreased abruptly to ~2 nA at ~−0.2 V, which indicated that the device had changed from the LRS to the HRS (the RESET process). In neuromorphic engineering, the device resistance can be mapped onto the weight of the synapse. The existence of these SET and RESET processes during the positive and negative voltage cycles (CC=1 mA), respectively, is the signature of the nonvolatile resistance of the device. As show in Figure S1, this conductance change of the device is nonvolatile. This nonvolatile property can be engineered into the LTP function of the synapse. The CC dependence of switching behaviors of the device was also studied (CC= 1 μA to 100 μA). The double-switching behavior occurred during every positive voltage sweep. However, under the low CCs (1 μA, 10 μA and 100 μA), an abrupt decrease in the current occurred at ~0.1V during the voltage sweep back to 0 V, which indicated that the device had spontaneously changed from the LRS to the HRS. And during the subsequent voltage sweep on the negative side (~10 ms interval after positive sweep), no RESET behavior occurred, which indicates that the retention time of the volatile switching is below 10 ms. Additionally, the cycle-tocycle (Figure S2) and the device-to-device (Figure S3) variability of these behaviors were confirmed, the results show that the devices had reproducible and reliable double switching characteristics under low CCs and a nonvolatile switching behavior under 1 mA CC. This CC dependent switching property indicates that the self-doping memristors can be worked at volatile or nonvolatile mode with an external current compliance control device, e.g. a transistor, which can also enable novel designs in neuromorphic computing to extend the circuits based on the memristors. With a low CC, the device shows STP, while a high CC, the shows LTP. The spontaneous conductance decay is also the foundation of the STP function in artificial synapse emulation. This spontaneous resistance decay behavior of the device can be observed more clearly during pulse measurements. As shown in Figure 1d, a 0.3 V amplitude and 100-μs-wide stimulating pulse was applied to the device, and was immediately followed 6 ACS Paragon Plus Environment

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with a 0.1 V amplitude and 200-μs-wide reading pulse to check the device state; the reading pulse was weak enough to ensure that it did not change the device state. After ~0.5 μs delay time (Figure S4) from the onset of the stimulating pulse, the device changed from the HRS to the LRS. The device can then return to the HRS spontaneously in the absence of the stimulating pulse after a relaxation time of ~9 μs. As shown in Figure 1e, the relaxation time increased to ~100 μs when the pulse amplitude increased to 0.6 V. We see that only one switching event occurs during application of stimulation pulses with low amplitudes (0.1 V and 0.6 V). With a high amplitude (0.9 V), we see that after the first switching event, a second switching event occurs at ~120 μs (Figure 1f). In the absence of the stimulating pulse, the device resistance decayed very slowly. The conductance retention test (Figure 1f insert) after the pulse stimulus shows that the device can remain in the LRS for a long time, which indicated that the device characteristics had changed from volatile to nonvolatile.

Figure 1 (a) Schematic diagram showing cross-sectional view of the Ag/Ag:Ta2O5/Pt selfdoping device. (b) Optical image of the device with two measurement probes. (c) I-V characteristics of the device during voltage sweeps with different CCs. Each positive voltage sweep was followed by a negative sweep at the same CC. (d)–(f) Current responses of the 7 ACS Paragon Plus Environment


device during pulse measurements at different amplitudes. The voltage sweep was applied to the Ag bottom electrode, while the Pt top electrode remained grounded during the measurements. The insert in (f) shows the conductance retention after the 0.9V pulse stimulus. 2.2 Switching Mechanism of Self-doping Memristors. To further understand the switching mechanism of our devices, a depth profile analysis of the Pt/Ta2O5:Ag/Ag selfdoping layer and a conventional Pt/Ag/Ta2O5 layer was carried out by the Auger electron spectroscopy (AES). As shown in Figure 2a, an Ag atom doping area is present at the interface between the Ta2O5 layer and the Ag layer (4 min – 6 min sputtering time), and the Ag atom concentration decreases as the Ta2O5 layer thickness increases. In the blank Ta2O5 layer (1.5 min – 4 min sputtering time), the Ag atom concentration is low. This self-doping phenomenon cannot occur in the conventional Pt/Ag/Ta2O5 layer (Figure 2b), where the interface between the Ag layer and the Ta2O5 layer is unambiguous and the Ag atom concentration gradient decreases sharply. With the self-doping phenomenon, the resistance of the pristine self-doping device is lower than that of the conventional Pt/Ta2O5/Ag device with the same Ta2O5 layer thickness. Because of the insulating property of the Ta2O5 film, an HRS with a resistance of GΩ-scale magnitude typically occurs with a 10 nm thickness of the Ta2O5 layer in the pristine Pt/Ta2O5/Ag device. However, as shown in Figure 2c, the pristine selfdoping memristor with a 10 nm Ta2O5 layer has a resistance of kΩ-scale magnitude. The relationship between the resistance and the Ta2O5 layer thickness appears to follow an exponential function (Note S1), which can be used to describe the resistance of oxide dielectrics with conductive carriers.48-49 This phenomenon also indicates that the resistance reduction in the pristine self-doping device is caused by the self-doping of the Ag atoms. As shown in Figure 2d, the I-V characteristics of the Pt/Ta2O5/Ag device (Figure 2e) were measured, where the Ta2O5 layer deposition parameters of the device was same as that of the 8 ACS Paragon Plus Environment

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device in Figure 1. To avoid the Ag self-doping phenomenon during the fabrication, we use the Ag as top electrode and Pt as bottom electrode. During the measurements, the voltage sweep was applied to the Pt bottom electrode while the Ag top electrode was grounded. Only one switching event occurs in the SET process, which happens during the negative voltage sweep. The switching threshold voltage is −1.2 V, which is obviously higher than that of the self-doping memristors, and the device’s threshold CC between volatile and nonvolatile behavior is lower than that of the self-doping memristors. During the negative sweeps with CC values of 1 μA and 10 μA, the device could return to the HRS spontaneously. In contrast, with the CC of 50 μA, the SET process became nonvolatile, and a RESET process occurred at ~0.7 V during the subsequent positive sweep. These characteristics differ significantly from those of the self-doping memristors. This is because the filaments formed among the thick blank Ta2O5 layer in Pt/Ta2O5/Ag devices is more difficult than that of Ag-doping devices, thus the threshold voltage is higher (−1.2 V). Meanwhile, this thick blank Ta2O5 layer also induces the Pt/Ta2O5/Ag device to form stronger and more stable filaments than Ag-doping device, which leads to a smaller threshold compliance current (CC) between volatile and nonvolatile behavior. To reproduce the double-switching behavior in the Pt/Ta2O5/Ag device, we inserted a TaOx layer with artificial Ag doping between the Ta2O5 layer and the Ag layer (Figure 2f) by a co-sputtering method. To make the device structure more similar to that of the self-doping memristor, the Ta2O5 layer thickness was reduced. As shown in Figure 2g, a double switching event occurred during the negative voltage sweep under the CC of 50 μA. The first switching event occurred at −0.2 V and a second switching event occurred at −0.5 V, which was similar to the behavior of the self-doping memristors. Then, during the subsequent voltage sweep on the positive side (~10 ms interval after negative sweep), the device remains in HRS, which indicates that the device relaxes from LRS to HRS within 10 ms. This volatile double switching also occurs in Pt/Ag:TaOx/Ag self-doping devices (Figure 1c) with low CCs 9 ACS Paragon Plus Environment


( < 100 uA). This is because that the filaments formed during the second switching are also relatively weak with a low CC (50 uA), thus they also rupture spontaneously in the absence of electric field. This spontaneous rupture phenomenon, as well as that in the self-doping devices, is achieved under the action of the Gibbs-Thomson effect or the interfacial energy minimization effect which was revealed by the high-resolution transmission electron microscopy (HRTEM) analysis.21, 50-53 Although the switching event of Pt/Ta2O5/Ag device, the second switching event of Pt/Ta2O5/Ag:TaOx/Ag artificial Ag-doping device and the selfdoping devices are all induced by the Ag filaments formed from the Ag electrode, their switching properties are different. The threshold voltage of the second switching events of artificial doping device (-0.5 V) and self-doping device (0.4 V) is smaller than that of Pt/Ta2O5/Ag device (-1.2V). By the doped Ag atoms in the Ta2O5 layer, the artificial doping device and self-doping device can first form a weak conductance filament, which induces the first switching event. With this weak filament, the Ag electrode can induce the second switching more easily than Pt/Ta2O5/Ag device, the later needs form filaments among a thicker blank Ta2O5 layer than the former. Further experimental results of the self-doping devices and the artificial Ag-doping devices are shown in Figure S5. All these results indicate that the self-doping phenomenon plays an important role in the double-switching behavior. We also performed a study of resistive switching with self-doping memristors of different sizes (Figure S6). The results show that the conductance after the first switching event is independent with the device size, which indicates that the switching behavior is determined by the filament mechanism.50 From the results described above, we can conclude that the switching mechanism in our selfdoping memristors can be described as follows. In the presence of an electric field, the Ag self-doping atoms can initially be oxidized to Ag+, and the electric force then drives the migration of the Ag nanoparticles to the counter electrode. They then form a temporary Ag 10 ACS Paragon Plus Environment

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filament in the Ta2O5 layer after a reduction reaction occurs, which leads to the first switching event. However, the filament formed seems to be unstable at this stage, and when the electric field is removed, the filament ruptures spontaneously. In contrast, under application of a high voltage, a significant number of Ag atoms diffuse into the Ta2O5 layer from the Ag electrode, which leads to the formation of one (or several) strong stable filament(s). With these stable filament(s), the device is then in the LRS and the device conductance decays slowly, thus allowing a nonvolatile property to be achieved. This switching mechanism is significantly similar to the dynamic process of the synaptic Ca2+ ions for short-term memory (STM) and long-term memory (LTM). In the biological synapses, the dynamical balance of the Ca2+ concentration plays important roles in the synaptic plasticity. 39-41 For the STM, the synapses plasticity is induced by the dynamics of Ca2+ and neurotransmitters in pre-synapses (Figure 2h).39 The Ca2+ concentration is determined by the influx via voltage-sensitive calcium channels and the extrusion via the plasma membrane Ca2+-ATPase.39 Upon application of a stimulus, the voltage-gated calcium channels of the pre-synaptic terminal are activated. This allows an influx of Ca2+ into the pre-synaptic terminal, which then triggers the fusion of the synaptic vesicle and results in the release of neurotransmitters into the cleft. These released neurotransmitters will then bind with the N-methyl-D-aspartate (NMDA) receptor at the postsynaptic terminal, which will cause the post-synaptic membrane potential to change. When the external stimulus is removed, the Ca2+ extrusion processes will occur and the neurotransmitters in the cleft will recover back to the pre-synaptic terminal at the same time, then the post-synaptic membrane potential recovers to a basal level. For self-doping memristors, the dynamics of the self-doping Ag atoms and the assembled conductive filaments resemble that of pre-synaptic Ca2+ and neurotransmitters, respectively. Upon application of a weak external stimulus, the electric field drives the self-doping Ag atoms into the pure Ta2O5 layer, which is similar to the pre-synaptic Ca2+ influx process. Then the 11 ACS Paragon Plus Environment


accumulated Ag atoms form a filament and lead to the increase of the device conductance, which is similar to the pre-synaptic activation function of neurotransmitters. Meanwhile, this conductance increase is similar to the synaptic membrane potential enhancement. When the external stimulus is removed, the accumulated Ag atoms diffuse by interfacial energy or Gibbs-Thomson effect,21 which is similar to the pre-synaptic Ca2+ extrusion process. Meanwhile the Ag filament ruptures spontaneously, which is analogous to the spontaneous recovery process of a neurotransmitter from the synaptic cleft (Figure S7). And the device conductance decreases to a low state, which corresponds to the basal level synaptic membrane potential. As for the LTM, the synaptic plasticity is induced by the dynamics of Ca2+ and the NMDA receptor in post-synapses (Figure 2i). 39 When the voltage-gated calcium channels of the post-synaptic terminal are activated by the external stimulus, a post-synaptic terminal Ca2+ influx occurred. This Ca2+ elevation activates more NMDA receptors, which can then bind with the neurotransmitters for a long time, thus inducing the LTM. For self-doping memristors, the dynamics of the Ag atoms from Ag electrode and the assembled conductive filaments resemble that of post-synaptic Ca2+ and neurotransmitters, respectively. Upon application of a strong external stimulus, the electric field drives the Ag atoms from the Ag electrode to the pure Ta2O5 layer, which is similar to the post-synaptic Ca2+ influx process. These Ag atoms finally combine with those from the self-doping Ag layer to form a strong and stable filament and cause the device conductance to increase for a long time; this is similar to the post-synaptic activation function of the long-term bind between neurotransmitters and NMDA receptors. And this nonvolatile conductance increase is similar to the long-term membrane potential enhancement.

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Figure 2 (a)–(b) Depth profiling results for samples of the Pt/Ta2O5:Ag/Ag self-doping layer and the normal Pt/Ag/Ta2O5 layer, as obtained by AES. (c) Resistance of the pristine selfdoping memristors at different Ta2O5 layer thicknesses. (d) I-V characteristics of the conventional Pt/Ta2O5/Ag device during voltage sweeps with different CCs. (e)–(f) Schematic diagrams showing cross-sectional views of the conventional Pt/Ta2O5/Ag device structure and the Pt/Ta2O5/Ag:TaOx/Ag artificial Ag-doping device structure, respectively. (g) I-V characteristics of the Pt/Ta2O5/Ag:TaOx/Ag artificial Ag-doping device. The negative voltage sweep was followed by a positive sweep at the same CC in (d) and (g). (h)–(i) Schematic illustration of STP and LTP synaptic emulations, respectively, comparing the biological synapse with the self-doping memristors.



2.3 Essential Synaptic Behaviors Demonstrated by the Devices. In the human brain, synapse plasticity is a spike-dependent behavior. One basic rule of the synaptic plasticity is the spike-rate-dependent plasticity (SRDP),54-55 which indicates that the synaptic weight is modified by the spike rate. To investigate emulation of the SRDP behavior using our memristors, multiple pulse stimuli that were separated by different intervals were applied to the device. Figure 3a shows the device responses to the different stimulation rates (i.e., the pulse intervals). The pulse amplitude was 0.6 V and its width was 10 μs. A 0.1 V continuous reading bias was applied during each interval to check the device state. Stimulation at 10 μs intervals (high rate) causes a clear current increase, whereas stimulation at 80 μs intervals (low rate) causes negligible current changes. This SRDP behavior is illustrated more clearly in Figure 3b and Figure S8, which show a clear stimulus rate dependence for the current increase (ΔI) enhancement. At a high stimulation rate, the stimulus is more effective, while a low-rate stimulus is less effective. In a biological synapse, when a spike is applied, an influx of Ca2+ ions is induced, and then the synapse connection is thus enhanced. The Ca2+ concentration requires time to decay to its original state, which is called the decay time. If another spike is applied at an interval that is below the decay time, the synapse connection will be stronger than the previous connection; this behavior is called paired-pulse facilitation (PPF).56-57 With of this behavior, the synapse connection will increase continually if many sequential spikes are applied over a short time; this is called post-tetanic potentiation (PTP).20,58 Figure 3c shows the PPF and PTP behaviors of our devices, which are similar to synaptic behaviors that were observed in previous biological studies59 and artificial synapses.20,30 And the switching behavior of our devices is faster (μs) than the biological synapse (ms)

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and some artificial synapses (ms)

20-21, 30

, the same trend of the behavior

implies the potential for more efficient artificial neuromorphic systems based on our devices. All these results indicate that the SRDP function of a biological synapse can be mimicked 14 ACS Paragon Plus Environment

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using our devices. In biological psychological systems, human memory can be divided into the STM and the LTM, 8-9 which correspond to the STP and the LTP in the synapse plasticity, respectively. In the STM, the information is stored temporarily for as little as a few seconds. In the LTM, the information can be maintained for a relatively long time. It should be noted that the LTM still fades with time; however, the rate of the decay is much slower than that of the STM. The STM content can be transformed into LTM content by frequently repeated rehearsal of events; this is the well-known STM-to-LTM transition.

9, 19

In Figure 3b, the ΔI

comes from two components: the STP (volatile switching) and the LTP (nonvolatile switching). We extracted the conductance change caused by the nonvolatile characteristic by excluding the data from the first 9 μs (relaxation time) after each pulse. Use of this method allowed the sharp decay in the LTP effect to be eliminated, with results as shown in Figure 3d. Comparison with Figure 3b shows that an STP-to-LTP transition clearly occurs under stimuli at 40 μs interval. For the first four pulses, the device is in the STP state, where the conductance increases negligibly with increasing pulse numbers. During the subsequent pulses, the conductance gradually increases with increasing pulse numbers. The LTP behavior is insignificant at the low stimulation rate (80 μs interval), while at the higher stimulation rates (10 and 20 μs intervals), an efficient LTP effect occurs. As shown in Figure 3e, the LTP efficiency was enhanced with an increase in the stimulation rate. At higher stimulation rates, the device conductance decayed more slowly than at low rates. The voltage sweep and pulse measurement results show that the stimulation amplitude is an important factor that can affect the device plasticity (either STP or LTP). Therefore, a study of the pulse amplitude dependence of the device’s STP and LTP behavior was performed. As shown in Figure 3f, under a low amplitude stimulus (0.3 V), only volatile resistance switching occurred, even at a high stimulation rate (10 μs interval). We can infer that a weak conductance increase occurred during the stimulation time, and the device conductance subsequently decayed to its initial 15 ACS Paragon Plus Environment


state during the interval. Further details of the results of application of the 0.3 V stimuli are shown in Figures S9 and S10. However, under application of a high stimulus (0.9 V), clear current increases occurred, even at the low stimulation rate (80 μs interval), as shown in Figure 3g. This result indicates that nonvolatile resistance changes are achieved at the high stimulation amplitudes. Further current response results for 0.9 V stimuli are shown in Figures S11 and S12. We also excluded the nonvolatile component of the conductance change after the stimulus (Figure 3h), which shows that the current increase is mainly caused by the LTP characteristic. We can also see that a high rate stimulus is more efficient in increasing the resistance than a low rate stimulus, which indicates that SRDP behavior is achieved in our device. In addition, both PPF and PTP behavior can also be achieved by our device (see Figure S12). As shown in Figure 3i, the LTP efficiency was enhanced by increases in both the amplitude and the stimulation rate. Under application of a highamplitude and high-rate stimulus, the device conductance decayed more slowly than it would under application of a low-amplitude and low-rate stimulus.


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Figure 3 (a), (f), (g) The current response of the device under continuous stimulating pulse. (b) Average current increase (ΔI) after each pulse at different pulse intervals. Here, ΔI is calculated by offsetting the current of each pulse with respect to the first pulse. (c) Average ΔI after consecutive pulses (I2 − I1) and after the eighth pulse (I8 − I1), which indicate PPF and PTP, respectively. The lines were fitted using an exponential function. (d), (h) Average conductance during the pulse intervals. The insert in (d) shows a schematic of the psychological model of human memory. (e), (i) Device conductance before stimulation, at the final stimulus and after stimulation. The amplitudes of each of the data are the averages of ten device measurements and the error bar represents the standard deviation in each case. To identify the nanoscale parallelisms between our device and the subcellular synaptic processes, we also performed nanoparticle simulations to capture the Ag nanoparticle 17 ACS Paragon Plus Environment


dynamics, which plays a central role in the varying current responses. The model is based on the Langevin equation, and takes the electrical, nano-mechanical and thermal degrees of freedom of the Ag nanoparticles into account (Note S2). As shown in Figure 4a, the double switching behavior is replicated. In a conventional Pt/Ta2O5/Ag memristor (Figure S13), the Ag nanoparticles are confined near the Ag electrode in the pristine state. As shown in Figure 4b, state 1, because of the self-doping phenomenon, the density of the Ag nanoparticles in self-doping memristors is more widely distributed. In this state, the device is also in the HRS. However, its resistance is lower than that of the Pt/Ta2O5/Ag memristor. As shown in Figure 4b, state 2, in the presence of an electric field, the Ag atoms can be oxidized to Ag+; when the sum of the electric force and the thermal random force of the atoms is stronger than the potential force, these Ag nanoparticles will migrate to the counter electrode. The Ag atoms that are located far from the electrode, which correspond to the self-doping atoms, migrate first because they are under a small potential force. Then, a weak filament forms, which leads to the first switching event. Because the self-doping Ag atoms represent a non-uniform and unsteady Ag source, the resulting filament is weak and will rupture spontaneously in the absence of the electric field. However, under a higher voltage, the stronger Joule heating effect and electric field force can induce diffusion of the Ag nanoparticles from the Ag electrode and cause them to be more widely dispersed. As shown in Figure 4b, state 3, with a more uniform and steady Ag source, the filaments are stronger and more stable than the previous filament, which leads to another switching event. We also see that the device returns to the HRS spontaneously after the voltage becomes zero, which is induced by the spontaneous rupture of conductive filaments in the absence of electric field. Wang et al. carried out an in situ HRTEM observation of the switching process of artificial Ag doped SiOxNy devices with volatile switching,21 which is much similar as our devices in the volatile mode. According to their observation, the doped Ag atoms can form a weak filament under 18 ACS Paragon Plus Environment

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the external electric field. In the absence of the electric field, the Ag filament spontaneously ruptures by a diffusion process, which is driven by the interfacial energy minimization. Simulations of the SRDP behavior and the pulse amplitude dependences of both the STP and the LTP were also performed. As shown in Figure 4c and 4d, under pulse stimuli at the same normalized amplitude (0.85) and width (0.15), STP behavior occurred at a low rate (normalized 0.35 interval), while at a higher rate (normalized 0.1 interval), a transition occurred between the STP and LTP behaviors. As shown in Figure 4c and 4e, we see that at the same stimulation rate (normalized 0.35 interval), a higher pulse amplitude can drive LTP behavior, while STP occurs at low amplitudes. As shown in Figure 4d and 4f, we see that under low amplitude (normalized 0.7) conditions, the current increase is negligible even at a high stimulation rate (normalized 0.1 interval), which indicates that LTP is achieved by the device. All these results are consistent with the trends observed in our experimental measurements.

Figure 4 (a) Simulated I-V characteristics of self-doping memristor with double-switching behavior. The normalized CC of the voltage sweep is 0.4. (b) Simulation results for the Ag 19 ACS Paragon Plus Environment


nanoparticle density at selected instants during the voltage sweep, corresponding to the points labelled in (a). The inset figures below show schematic diagrams of the Ag nanoparticle positions. (c)–(f) Simulation results for the current response at different stimulation amplitudes and intervals. 2.4 Neuromorphic Computing Based on Self-doping Memristors. To demonstrate neuromorphic system applications, a neural network with an STP-to-LTP transition (S2L) layer based on our devices was proposed and simulated. This network has two parts (Figure 5a): one is an S2L layer consists of 784 × 1 self-doping memristors, while the other is a perceptron based on a 784 × 784 conventional nonvolatile memristor array. We used the MNIST-backrand dataset to train and test the network performance. Unlike the common MNIST dataset, MNIST-backrand is a dataset that adds strong random noise to each figure, which leads to a more difficult recognition problem. Noise is a common problem in practical figure recognition applications that can significantly reduce the accuracy of neural networks. Our network with an S2L layer can solve this noisy classification task efficiently. A simplified STP to LTP transition model of the self-doping device was introduced to build the neural network (Note S3) and the simulation results are shown in Figure S14. In this model, we assume that the variability of the device is Gaussian distribution. The network’s learning process occurs in four stages (Figure 5b). In the first stage, the S2L layer is pre-trained while the perceptron part is inhibited (Figure 5c). In this stage, noisy examples are input into the S2L layer at a constant interval (Δt). The gray-scale pixels of a picture are mapped into the amplitude of spikes. Each picture contains 28 × 28 gray-scale pixels; and is translated into 784 × 1 input spikes. The gray-scale pixels are normalized to the range [0, 1] and the amplitude of input spikes are in the range [0, 0.6]. A 0.6 V spike represents a pure white pixel, whereas a black pixel corresponds to no spike (0 V). Using this method, the major feature pixels can stimulate the self-doping memristors at a high rate, which can induce them to reach 20 ACS Paragon Plus Environment

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the LTP level. In contrast, secondary feature pixels such as random noises stimulate the devices at a low rate that can only lead them to the STP level. After the pre-training process, no voltage is applied to the S2L layer for the forgetting period T. During this time, the selfdoping devices that are at the STP level will spontaneously decay to the HRS, which can be regarded as spontaneous forgetting of the secondary features. However, this forgetting time affects the major features inefficiently. The training of the perceptron part then begins. During this process, both of the S2L layer and the perceptron are active. In this process and the next testing process, the amplitudes of input spikes are mapped into the range [0, 0.06], which is weak enough that it does not disturb the device state of the S2L layer. The weight values of the perceptron store in the conventional nonvolatile memristor array. And the weight update during the training process is only occurred in the nonvolatile memristor array, while the S2L layer is not changed. The pictures are mapped into the stimulating pulse and are input through the S2L layer to the perceptron part. The perceptron training uses the gradient descent algorithm. As shown in Figure 5d, use of an appropriate Δt allows the network to perform better than the individual perceptron, with accuracy that is higher than that of the MNISTbackrand dataset with 50% noise. The network performance degrades if Δt is too small or too large. This occurs because, when Δt is too small, too many devices enter the LTP level, causing the conductance map of the S2L layer to be oversaturated (see Figure 5f). Therefore, the S2L layer has little effect on the input pictures and the network accuracy is approximate to that of the individual perceptron. In addition, when Δt is too large, insufficient numbers of devices are able to enter the LTP level to help retain the major features of the image (see Figure 5f). Conversely, when a picture is processed through the S2L transition layer, nearly all the important information is filtered, which makes classification more difficult. The forgetting time T also plays a role in network learning (Figure 5e). At the final stage of pretraining, some secondary feature pixels induced some devices to reach the STP level (Figure 21 ACS Paragon Plus Environment


5g). Given an appropriate forgetting time, these devices can then decay to the HRS spontaneously. If the forgetting time is too short, the devices do not have sufficient time to decay, but if the forgetting time is too long, some major features will also be forgotten; these two conditions are both detrimental to the network performance. However, because of the LTP behavior, the conductance map of the S2L layer will tend to be in a stable state, thus the network performance tends to be stable. And with an overshoot learning strategy, the network performance can tend to be in an optimum condition after forgetting (green line in Figure 5e). As shown in Figure 5h, when a suitable conductance map is provided, the noisy picture can be translated into a clearer picture, which significantly improves the network performance.

Figure 5 (a) Conceptual architectural diagram of the network with an S2L layer. (b) Learning process of the network. (c) Pre-training of the S2L layer. (d) Classification accuracy of the 22 ACS Paragon Plus Environment

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network as a function of interval time (Δt). (e) Classification accuracy of the network as a function of the forgetting time (T). Here, Δt is 10 μs. (f) Final conductance maps of the S2L layer for different Δt values. (g) Conductance maps of the S2L layer before pre-training, at the final pre-training stage and after pre-training. The Δt is 10 μs. (h) Noisy example picture of the “6” before and after the S2L layer. 3. CONCLUSIONS In this work, a self-doping memristor was fabricated to mimic the behavior of a biological synapse with a high level of bio-fidelity. Unique double-switching behavior was demonstrated during the SET process. A series of experiments and simulations showed that this behavior was realized via the Ag dynamics in the Ta2O5 layers, which formed a self-doping source and an electrode source and showed a significant resemblance to the Ca2+ dynamics in biological synapses. Using this self-doping memristor, we demonstrated that the important features of a biological synapse, including the STP-to-LTP transition, PPF, PTP and SRDP, could be realized by applying a continuous series of pulse stimuli to a single device. These behaviors are the fundamental functions of spike neural networks (SNNs), which indicate that our devices could be promising fundamental synaptic devices for future SNNs. Finally, a neural network with an S2L layer based on the self-doping memristor was introduced to illustrate the benefits of this STP-to-LTP transition property in a noisy image classification task. The results reported here demonstrate that our self-doping memristors enable emulation of artificial synapses with improved bio-fidelity and can thus be considered as important components for future neuromorphic computing systems. 4. EXPERIMENTAL SECTION



4.1 Device fabrication. The device was fabricated on the Si substrates with 300 nm thermally grown silicon dioxides (SiO2). All the films are deposited using a magnetron cosputtering system (AJA, USA) at room temperature in argon (Ar) atmosphere. Ag/Ag:Ta2O5/Pt self-doping devices were fabricated as following process. The 20 nm Ag bottom electrode with a 5 nm Ti adhesion layer was patterned by lithography, and fabricated by the direct current (DC) sputtering deposition and lift off process. The deposition of Ti layer and Ag layer was in a 3 mTorr and 1.5 mTorr Ar atmosphere, respectively. Then the 25 nm Ta2O5 layer and a 20 nm Pt layer was patterned by lithography, and fabricated by the radio frequency (RF) sputtering deposition and DC-sputtering deposition, respectively. The deposition of Ta2O5 layer and Pt layer was in a 5 mTorr and 3 mTorr Ar atmosphere. Finally, using a lift off process to form the Ag/Ag:Ta2O5/Pt structure. The power of the Ta2O5 deposition was 100 W, which can induce the self-doping of Ag atoms into the Ta2O5 layer. The results of the AES study can also indicate this phenomenon. For normal Ag/Ta2O5/Pt device, the 25 nm Ta2O5 layer was deposited before the Ag layer by RF-sputtering. Because the power of the Ag layer deposition is low (50 W), the Ag atoms cannot be doped into the Ta2O5 layer. The AES study can also support this conclusion. For Pt/Ta2O5/Ag:TaOx/Ag artificial Ag doping device, the Ta2O5 layer was reduced to 10 nm and a 15 nm artificial Ag doping layer was deposited by Ag and Ta co-sputtering in a 5 mTorr Ar and oxygen (O2) mixed atmosphere. The ratio of the flow between Ar and O2 is 5:2. The power of the Ag and Ta deposition are 50 W and 150 W, respectively. 4.2 AES sample fabrication. The films were deposited on the Si substrates and the deposition condition is the same as the device fabrication. The Pt/Ta2O5:Ag self-doping layer sample was fabricated in the following process. First, a 20 nm Ag layer was deposited by the DC-sputtering with a 5 nm Ti adhesion layer. Then a 30 nm Ta2O5 layer was deposited by the RF-sputtering in 100 W power condition. Finally, a 20 nm Pt layer was deposited by the DC24 ACS Paragon Plus Environment

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sputtering to prevent further oxidation of the sample. The Pt/Ag/Ta2O5 normal layer sample fabrication process was opposite. 4.3 Electrical measurements. DC voltage sweeps were performed by the Keysight B1500A semiconductor analyzes system, which is equipped with the high-resolution source and measurement units with a specified resolution of 1 fA. The Keysight B1530A waveform generator/fast measurement unit (WGFMU) was used to perform the pulse measurements. Using a two-probe (W tips) configuration, DC and pulse voltages are applied upon one electrode with another electrode grounded. 4.4 Measurements of the depth profiles. We use the PHI 710 auger electron spectroscopy to analyze the depth profiles of our samples. ASSOCIATED CONTENT Supporting Information. Nonvolatile properties, endurance characteristics and device-to-device variability of the device; the delay time of the device under different pulse amplitudes; I-V characteristics of the Ag/Ag:Ta2O5/Ag self-doping device and the Pt/Ta2O5/Ag:TaOx/Pt artificial Ag doping device; I-V characteristics of the self-doping memristors with different size; schematic illustration of STP synaptic emulations comparing the biological synapse with the self-doping memristors; average current through the memristor recorded after each pulse at different pulse intervals; the current response of the device under continuous stimulating pulse at different pulse intervals; simulated I-V characteristic of normal Pt/Ta2O5/Ag memristor with single switching behavior; fitting results of the current response of the device under stimulus with 0.6 V amplitude and 10 μs intervals; fitting results of the conductance decay behavior are shown in Figures S1-S14. 25 ACS Paragon Plus Environment


The description of exponential function in the conductance fitting of self-doping memristor; the description of nanoparticle simulations of the device; the description of STP to LTP transition model of the device in the neural network simulation are shown in Notes S1-S3. AUTHOR INFORMATION Corresponding Author *E-mail: [email protected] ORCID Luping Shi: 0000-0002-9829-2202 Notes The authors declare no competing financial interest. Author Contributions L. Shi and Y. Wang conceived and directed this work. Y. Wang, Z. Zhang and M. Xu wrote the manuscript. Y. Wang, Z. Zhang, Y. Yang and M. Ma conducted the fabrication. Y. Wang, Z. Zhang and H. Li conducted the measurement. Y. Wang, M. Xu and J. Pei conducted the simulation. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. §These authors contributed equally. ACKNOWLEDGMENT This work was partly supported by National Nature Science Foundation of China (Nos. 61327902 and 61836004), Suzhou-Tsinghua innovation leading program (No. 2016SZ0102) and Brain-Science Special Program of Beijing under Grant Z181100001518006. REFERENCES (1) Kandel, E. R. The Molecular Biology of Memory Storage: A Dialogue between Genes 26 ACS Paragon Plus Environment

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Figure Captions Figure 1 (a) Schematic diagram showing cross-sectional view of the Ag/Ag:Ta2O5/Pt selfdoping device. (b) Optical image of the device with two measurement probes. (c) I-V characteristics of the device during voltage sweeps with different CCs. Each positive voltage sweep was followed by a negative sweep at the same CC. (d)–(f) Current responses of the device during pulse measurements at different amplitudes. The voltage sweep was applied to the Ag bottom electrode, while the Pt top electrode remained grounded during the measurements. The insert in (f) shows the conductance retention after the 0.9V pulse stimulus. Figure 2 (a)–(b) Depth profiling results for samples of the Pt/Ta2O5:Ag/Ag self-doping layer and the normal Pt/Ag/Ta2O5 layer, as obtained by AES. (c) Resistance of the pristine selfdoping memristors at different Ta2O5 layer thicknesses. (d) I-V characteristics of the conventional Pt/Ta2O5/Ag device during voltage sweeps with different CCs. (e)–(f) Schematic diagrams showing cross-sectional views of the conventional Pt/Ta2O5/Ag device structure and 33 ACS Paragon Plus Environment


the Pt/Ta2O5/Ag:TaOx/Ag artificial Ag-doping device structure, respectively. (g) I-V characteristics of the Pt/Ta2O5/Ag:TaOx/Ag artificial Ag-doping device. The negative voltage sweep was followed by a positive sweep at the same CC in (d) and (g). (h)–(i) Schematic illustration of STP and LTP synaptic emulations, respectively, comparing the biological synapse with the self-doping memristors. Figure 3 (a), (f), (g) The current response of the device under continuous stimulating pulse. (b) Average current increase (ΔI) after each pulse at different pulse intervals. Here, ΔI is calculated by offsetting the current of each pulse with respect to the first pulse. (c) Average ΔI after consecutive pulses (I2 − I1) and after the eighth pulse (I8 − I1), which indicate PPF and PTP, respectively. The lines were fitted using an exponential function. (d), (h) Average conductance during the pulse intervals. The insert in (d) shows a schematic of the psychological model of human memory. (e), (i) Device conductance before stimulation, at the final stimulus and after stimulation. The amplitudes of each of the data are the averages of ten device measurements and the error bar represents the standard deviation in each case. Figure 4 (a) Simulated I-V characteristics of self-doping memristor with double-switching behavior. The normalized CC of the voltage sweep is 0.4. (b) Simulation results for the Ag nanoparticle density at selected instants during the voltage sweep, corresponding to the points labelled in (a). The inset figures below show schematic diagrams of the Ag nanoparticle positions. (c)–(f) Simulation results for the current response at different stimulation amplitudes and intervals. Figure 5 (a) Conceptual architectural diagram of the network with an S2L layer. (b) Learning process of the network. (c) Pre-training of the S2L layer. (d) Classification accuracy of the network as a function of interval time (Δt). (e) Classification accuracy of the network as a function of the forgetting time (T). Here, Δt is 10 μs. (f) Final conductance maps of the S2L 34 ACS Paragon Plus Environment

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layer for different Δt values. (g) Conductance maps of the S2L layer before pre-training, at the final pre-training stage and after pre-training. The Δt is 10 μs. (h) Noisy example picture of the “6” before and after the S2L layer.



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Self-Doping Memristors with Equivalently Synaptic Ion Dynamics for

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